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CAIP - Certified Artificial Intelligence Practitioner
Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions.
Provider Information
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Contact Information
Global Knowledge
9000 Regency Parkway
Cary, NC 27518
Course Overview
Overall Proficiency Level
2 - Intermediate
Course Catalog Number
840033
Course Prerequisites
To ensure your success in this course, you should be familiar with the concepts that are foundational to data science, including:
- The overall data science and machine learning process from end to end: formulating the problem; collecting and preparing data; analyzing data; engineering and preprocessing data; training, tuning, and evaluating a model; and finalizing a model.
- Statistical concepts such as sampling, hypothesis testing, probability distribution, randomness, etc.
- Summary statistics such as mean, median, mode, interquartile range (IQR), standard deviation, skewness, etc.
- Graphs, plots, charts, and other methods of visual data analysis.
You must also be comfortable writing code in the Python programming language, including the use of fundamental Python data science libraries like NumPy and pandas.
Training Purpose
Functional Development
Skill Development
Specific Audience
All
Delivery Method
Classroom
Online, Instructor-Led
Course Location
9000 Regency Parkway
Cary, NC 27518
Learning Objectives
In this course, you will develop AI solutions for business problems. You will:
- Solve a given business problem using AI and ML
- Prepare data for use in machine learning
- Train, evaluate, and tune a machine learning model
- Build linear regression models
- Build forecasting models
- Build classification models using logistic regression and k -nearest neighbor
- Build clustering models
- Build classification and regression models using decision trees and random forests
- Build classification and regression models using support-vector machines (SVMs)
- Build artificial neural networks for deep learning
- Put machine learning models into operation using automated processes
- Maintain machine learning pipelines and models while they are in production
Framework Connections
The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):
Competency Areas
Feedback
If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.
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